Flowchart
library(GENIE3)
library(doParallel)
library(igraph)
library(tidyverse)
library(DT)
library(reticulate)
library(learn2count)
library(rbenchmark)
library(reshape2)
library(gridExtra)
library(DiagrammeR)
library(pROC)
use_python("/usr/bin/python3", required = TRUE)
arboreto <- import("arboreto.algo")
pandas <- import("pandas")
numpy <- import("numpy")
source("dropo.R")
source("generate_adjacency.R")
source("simmetric.R")
source("pscores.R")
source("plotg.R")
source("compare_consensus.R")
source("earlyj.R")
source("plotROC.R")
source("cutoff_adjacency.R")
source("infer_networks.R")
DiagrammeR::grViz("
digraph biological_workflow {
# Set up the graph attributes
graph [layout = dot, rankdir = TB]
# Define consistent node styles
node [shape = rectangle, style = filled, color = lightblue, fontsize = 12]
# Define nodes for each step
StartNode [label = 'Ground Thruth - String Regulatory Network', shape = oval, color = seagreen, fontcolor = black]
AdjacencyMatrix [label = 'Thruth Adjacency Matrix', shape = rectangle, color = seagreen]
SimulateData [label = 'Simulate Single-Cell Data', shape = rectangle, color = goldenrod]
# Reconstruction using Three Packages
LateIntegration [label = 'Late\nIntegration', shape = oval, color = khaki]
EarlyIntegration [label = 'Early\nIntegration', shape = oval, color = khaki]
Jointanalysis [label = 'Joint\nanalysis', shape = oval, color = khaki]
# Process
earlyj [label = 'earlyj.R', shape=diamond, color=lightblue, fontcolor=black]
networkinference [label = 'infer_networks.R\nGENIE3\nGRNBoost2\nJRF', shape = rectangle, color = goldenrod, fontcolor=black]
simmetric [label = 'simmetric.R', shape = rectangle, color = goldenrod, fontcolor=black]
plotROC [label = 'plotROC.R', shape=diamond, color=lightblue, fontcolor=black]
generateadjacency [label='generate_adjacency.R\nWeighted Adjacency', shape=rectangle, color=goldenrod, fontcolor=black]
cutoffadjacency [label='cutoff_adjacency.R\nBinary Adjacency', shape=rectangle, color=goldenrod, fontcolor=black]
pscores [label='pscores.R\nTPR\nFPR\nF1\nAccuracy\nPrecision', shape=diamond, color=lightblue, fontcolor=black]
voting [label='Edges voting', shape=diamond, color=lightblue, fontcolor=black]
plotgcompare [label='plotg.R\ncompare_consesus.R\nPlot Graphs', shape=rectangle, color=goldenrod, fontcolor=black]
# Define the workflow structure
StartNode -> AdjacencyMatrix
AdjacencyMatrix -> SimulateData
SimulateData -> LateIntegration
SimulateData -> EarlyIntegration
SimulateData -> Jointanalysis
EarlyIntegration -> earlyj
earlyj -> networkinference
LateIntegration -> networkinference
Jointanalysis -> networkinference
networkinference -> simmetric
simmetric -> plotROC
simmetric -> generateadjacency
generateadjacency -> cutoffadjacency
cutoffadjacency -> pscores
cutoffadjacency -> voting
voting -> plotgcompare
}
")
Tcell Ground Truth
adjm <- read.table("./../data/adjacency_matrix.csv", header = T, row.names = 1, sep = ",") %>% as.matrix()
adjm %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "Ground Truth")
gtruth <- igraph::graph_from_adjacency_matrix(adjm, mode = "undirected", diag = F)
num_nodes <- vcount(gtruth)
num_edges <- ecount(gtruth)
set.seed(1234)
plot(gtruth,
main = paste("Ground Truth\nNodes:", num_nodes, "Edges:", num_edges),
vertex.label.color = "black",
vertex.size = 6,
edge.width = 2,
vertex.label = NA,
vertex.color = "steelblue",
layout = igraph::layout_with_fr)
Simulate Data
ncell <- 500
nodes <- nrow(adjm)
set.seed(1130)
mu_values <- c(1.5, 3, 5)
count_matrices <- lapply(1:3, function(i) {
set.seed(1130 + i)
mu_i <- mu_values[i]
count_matrix_i <- simdata(n = ncell, p = nodes, B = adjm, family = "ZINB",
mu = mu_i, mu_noise = 1, theta = 1, pi = 0.2)
count_matrix_df <- as.data.frame(count_matrix_i)
colnames(count_matrix_df) <- colnames(adjm)
rownames(count_matrix_df) <- paste("cell", 1:nrow(count_matrix_df), sep = "")
return(count_matrix_df)
})
count_matrices[[1]] %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "Simulated count matrix")
saveRDS(count_matrices, "./../analysis/count_matrices.RDS")
Matrices Integration
Late Integration
GENIE3
set.seed(1234)
genie3_late <- infer_networks(count_matrices, method="GENIE3")
saveRDS(genie3_late, "./../analysis/genie3_late.RDS")
genie3_late[[1]] %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "GENIE3 output")
Simmetric Output and ROC
sgenie3_late <- simmetric(genie3_late, weight_function = "mean")
plotROC(sgenie3_late, adjm, plot_title = "ROC curve - GENIE3 Late Integration")
sgenie3_late[[1]] %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "GENIE3 simmetric output")
Generate Adjacency and Apply Cutoff
sgenie3_late_wadj <- generate_adjacency(sgenie3_late)
sgenie3_late_adj <- cutoff_adjacency(count_matrices = count_matrices,
weighted_adjm_list = sgenie3_late_wadj,
n = 2,
method = "GENIE3")
## Matrix 1 Mean 95th Percentile Cutoff: 0.01
## Matrix 2 Mean 95th Percentile Cutoff: 0.01
## Matrix 3 Mean 95th Percentile Cutoff: 0.01
sgenie3_late_wadj[[1]] %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "GENIE3 weight adjacency")
sgenie3_late_adj[[1]] %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "GENIE3 adjacency")
Comparison with the Ground Truth
scores <- pscores(adjm, sgenie3_late_adj)
scores$Statistics %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "scores")
plots <- plotg(sgenie3_late_adj)
ajm_compared <- compare_consensus(sgenie3_late_adj, adjm)
GRNBoost2
set.seed(1234)
grnb_late <- infer_networks(count_matrices, method="GRNBoost2")
saveRDS(grnb_late, "./../analysis/grnb_late.RDS")
grnb_late[[1]] %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "GRNBoost2 output")
Simmetric Output and ROC
sgrnb_late <- simmetric(grnb_late, weight_function = "mean")
plotROC(sgrnb_late, adjm, plot_title = "ROC curve - GRNBoost2 Late Integration")
sgrnb_late[[1]] %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "GRNBoost2 simmetric output")
Generate Adjacency and Apply Cutoff
sgrnb_late_wadj <- generate_adjacency(sgrnb_late)
sgrnb_late_adj <- cutoff_adjacency(count_matrices = count_matrices,
weighted_adjm_list = sgrnb_late_wadj,
n = 2,
method = "GRNBoost2")
## Matrix 1 Mean 95th Percentile Cutoff: 0.989
## Matrix 2 Mean 95th Percentile Cutoff: 0.962
## Matrix 3 Mean 95th Percentile Cutoff: 0.93
sgrnb_late_wadj[[1]] %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "GRNBoost2 weight adjacency")
sgrnb_late_adj[[1]] %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "GRNBoost2 adjacency")
Comparison with the Ground Truth
scores <- pscores(adjm, sgrnb_late_adj)
scores$Statistics %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "scores")
plots <- plotg(sgrnb_late_adj)
ajm_compared <- compare_consensus(sgrnb_late_adj, adjm)
Early Integration
early_matrix <- list(earlyj(count_matrices))
GENIE3
set.seed(1234)
genie3_early <- infer_networks(early_matrix, method="GENIE3")
saveRDS(genie3_early, "./../analysis/genie3_early.RDS")
genie3_early[[1]] %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "GENIE3 output")
Simmetric Output and ROC
sgenie3_early <- simmetric(list(genie3_early[[1]]), weight_function = "mean")
plotROC(sgenie3_early, adjm, plot_title = "ROC curve - GENIE3 Early Integration")
sgenie3_early[[1]] %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "GENIE3 simmetric output")
Generate Adjacency and Apply Cutoff
sgenie3_early_wadj <- generate_adjacency(sgenie3_early)
sgenie3_early_adj <- cutoff_adjacency(count_matrices = list(early_matrix[[1]]),
weighted_adjm_list = sgenie3_early_wadj,
n = 2,
method = "GENIE3")
## Matrix 1 Mean 95th Percentile Cutoff: 0.01
sgenie3_early_wadj[[1]] %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "GENIE3 weight adjacency")
sgenie3_early_adj[[1]] %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "GENIE3 adjacency")
Comparison with the Ground Truth
scores <- pscores(adjm, sgenie3_early_adj)
scores$Statistics %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "scores")
plots <- plotg(sgenie3_early_adj)
ajm_compared <- compare_consensus(sgenie3_early_adj, adjm)
GRNBoost2
set.seed(1234)
grnb_early <- infer_networks(early_matrix, method="GRNBoost2")
saveRDS(grnb_early, "./../analysis/grnb_early.RDS")
grnb_early[[1]] %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "GRNBoost2 output")
Simmetric Output and ROC
sgrnb_early <- simmetric(list(grnb_early[[1]]), weight_function = "mean")
plotROC(sgrnb_early, adjm, plot_title = "ROC curve - GRNBoost2 Early Integration")
sgrnb_early[[1]] %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "GRNBoost2 simmetric output")
Generate Adjacency and Apply Cutoff
sgrnb_early_wadj <- generate_adjacency(sgrnb_early)
sgrnb_early_adj <- cutoff_adjacency(count_matrices = list(early_matrix[[1]]),
weighted_adjm_list = sgrnb_early_wadj,
n = 2,
method = "GRNBoost2")
## Matrix 1 Mean 95th Percentile Cutoff: 4.84
sgrnb_early_wadj[[1]] %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "GRNBoost2 weight adjacency")
sgrnb_early_adj[[1]] %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "GRNBoost2 adjacency")
Comparison with the Ground Truth
scores <- pscores(adjm, sgrnb_early_adj)
scores$Statistics %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "scores")
plots <- plotg(sgrnb_early_adj)
ajm_compared <- compare_consensus(sgrnb_early_adj, adjm)
Joint Integration
Joint Random Forest
#https://cran.r-project.org/src/contrib/Archive/JRF/
#install.packages("/home/francescoc/Downloads/JRF_0.1-4.tar.gz", repos = NULL, type = "source")
library(JRF)
jrf_matrices <- count_matrices
jrf_matrices[[1]] <- t(jrf_matrices[[1]])
jrf_matrices[[2]] <- t(jrf_matrices[[2]])
jrf_matrices[[3]] <- t(jrf_matrices[[3]])
result <- JRF(X=jrf_matrices, genes.name = rownames(jrf_matrices[[1]]), ntree = 500, mtry = round(sqrt(length(rownames(jrf_matrices[[1]]))-1)))
result %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "JRF output")
sessionInfo()
## R version 4.1.0 (2021-05-18)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.2 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=it_IT.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=it_IT.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=it_IT.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=it_IT.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] JRF_0.1-4 pROC_1.18.0 DiagrammeR_1.0.11 gridExtra_2.3
## [5] reshape2_1.4.4 rbenchmark_1.0.0 learn2count_0.3.2 reticulate_1.34.0
## [9] DT_0.22 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.9
## [13] purrr_0.3.4 readr_2.1.2 tidyr_1.2.0 tibble_3.1.7
## [17] ggplot2_3.3.6 tidyverse_1.3.1 igraph_2.0.3 doParallel_1.0.17
## [21] iterators_1.0.14 foreach_1.5.2 GENIE3_1.16.0
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-7 matrixStats_0.62.0
## [3] fs_1.5.2 lubridate_1.8.0
## [5] RColorBrewer_1.1-3 httr_1.4.3
## [7] GenomeInfoDb_1.30.1 tools_4.1.0
## [9] backports_1.4.1 bslib_0.3.1
## [11] iZID_0.0.1 utf8_1.2.2
## [13] R6_2.5.1 DBI_1.1.2
## [15] BiocGenerics_0.40.0 colorspace_2.0-3
## [17] withr_2.5.0 tidyselect_1.1.2
## [19] compiler_4.1.0 graph_1.72.0
## [21] Biobase_2.54.0 cli_3.3.0
## [23] rvest_1.0.2 xml2_1.3.3
## [25] DelayedArray_0.20.0 labeling_0.4.2
## [27] sass_0.4.1 scales_1.2.0
## [29] digest_0.6.29 rmarkdown_2.14
## [31] XVector_0.34.0 pkgconfig_2.0.3
## [33] htmltools_0.5.2 MatrixGenerics_1.6.0
## [35] highr_0.9 dbplyr_2.1.1
## [37] fastmap_1.1.0 htmlwidgets_1.5.4
## [39] rlang_1.1.4 readxl_1.4.0
## [41] rstudioapi_0.13 farver_2.1.0
## [43] visNetwork_2.1.2 jquerylib_0.1.4
## [45] generics_0.1.2 jsonlite_1.8.0
## [47] crosstalk_1.2.0 RCurl_1.98-1.6
## [49] magrittr_2.0.3 GenomeInfoDbData_1.2.7
## [51] Matrix_1.6-1.1 Rcpp_1.0.8.3
## [53] munsell_0.5.0 S4Vectors_0.32.4
## [55] fansi_1.0.3 lifecycle_1.0.1
## [57] stringi_1.7.6 yaml_2.3.5
## [59] distributions3_0.2.2 zlibbioc_1.40.0
## [61] MASS_7.3-57 SummarizedExperiment_1.24.0
## [63] plyr_1.8.7 grid_4.1.0
## [65] crayon_1.5.1 lattice_0.20-45
## [67] haven_2.5.0 hms_1.1.1
## [69] knitr_1.39 pillar_1.7.0
## [71] GenomicRanges_1.46.1 codetools_0.2-18
## [73] stats4_4.1.0 reprex_2.0.1
## [75] glue_1.6.2 evaluate_0.15
## [77] modelr_0.1.8 png_0.1-7
## [79] vctrs_0.4.1 tzdb_0.3.0
## [81] cellranger_1.1.0 gtable_0.3.0
## [83] assertthat_0.2.1 xfun_0.30
## [85] broom_0.8.0 SingleCellExperiment_1.16.0
## [87] IRanges_2.28.0 ellipsis_0.3.2